research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer
Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""MRV09 — CCI Reversion
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HYPOTHESIS: CCI(20) < -100 signals oversold -> go LONG. Exit when CCI > 0 (mean reversion).
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Trend gate: only buy when price is above 200-day SMA (long-term uptrend confirmation).
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CCI (Commodity Channel Index) = (TypicalPrice - SMA(TP, n)) / (0.015 * MeanAbsDev(TP, n))
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Extreme readings (<-100) indicate oversold conditions; reversal expected.
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CAUSAL: CCI at bar i uses data up to and including close[i].
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Entry at close[i] when CCI[i] < -100 (AND trend gate: close[i] > SMA200[i]).
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Exit at close[i] when CCI[i] > 0.
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SL: ATR-based (entry - 2*ATR) to limit downside.
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max_bars: cap position holding time.
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Small grid: (cci_period, max_bars) -> 4 configs, 1d only.
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import altlib as al
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import numpy as np
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import pandas as pd
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def cci(df: pd.DataFrame, period: int = 20) -> np.ndarray:
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"""Commodity Channel Index (causal).
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CCI = (TP - SMA(TP, n)) / (0.015 * MeanAbsDev(TP, n))
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where TP = (high + low + close) / 3
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"""
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h = df["high"].values.astype(float)
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l = df["low"].values.astype(float)
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c = df["close"].values.astype(float)
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tp = (h + l + c) / 3.0
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n = len(tp)
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cci_vals = np.full(n, np.nan)
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for i in range(period - 1, n):
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window = tp[i - period + 1:i + 1]
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m = np.mean(window)
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mad = np.mean(np.abs(window - m))
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if mad > 0:
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cci_vals[i] = (tp[i] - m) / (0.015 * mad)
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else:
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cci_vals[i] = 0.0
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return cci_vals
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def make_entries(df, cci_period=20, sma_period=200, sl_atr_mult=2.0, max_bars=10, use_trend_gate=True):
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"""
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Entry: CCI[i] < -100 (oversold), optionally gated by close > SMA200 (uptrend).
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Exit: CCI[i] > 0 (mean reversion complete), or SL or max_bars.
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All causal: uses data up to and including close[i].
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"""
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c = df["close"].values.astype(float)
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n = len(df)
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# CCI (causal, computed above)
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cci_vals = cci(df, cci_period)
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# SMA200 for trend gate
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sma200 = al.sma(c, sma_period)
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# ATR for SL
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atr_vals = al.atr(df, win=14)
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entries = [None] * n
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for i in range(sma_period, n):
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ci = cci_vals[i]
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if np.isnan(ci):
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continue
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# Trend gate: only long when price is above long-term SMA
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if use_trend_gate and (np.isnan(sma200[i]) or c[i] <= sma200[i]):
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continue
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# Oversold condition
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if ci >= -100.0:
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continue
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# Entry at close[i], long
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entry_px = c[i]
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sl_px = entry_px - sl_atr_mult * atr_vals[i]
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# Sanity check: SL must be below entry
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if sl_px >= entry_px:
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continue
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entries[i] = {"dir": +1, "tp": None, "sl": sl_px, "max_bars": max_bars}
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return entries
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# -----------------------------------------------------------------------
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# Grid: small (4 configs total, 1d only -> 4 * 2 assets = 8 backtests)
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# -----------------------------------------------------------------------
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CONFIGS = [
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# (cci_period, sma_period, sl_atr_mult, max_bars, use_trend_gate, label)
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(20, 200, 2.0, 10, True, "cci20_sma200_sl2atr_10d"),
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(20, 200, 2.0, 20, True, "cci20_sma200_sl2atr_20d"),
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(14, 200, 1.5, 10, True, "cci14_sma200_sl1.5atr_10d"),
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(20, 200, 2.0, 10, False, "cci20_nosma_sl2atr_10d"), # no trend gate control
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]
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best_rep = None
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best_min_hold = -999.0
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for cci_period, sma_period, sl_atr_mult, max_bars, use_trend_gate, label in CONFIGS:
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name = f"MRV09-{label}"
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def make_fn(cp=cci_period, sp=sma_period, sa=sl_atr_mult, mb=max_bars, utg=use_trend_gate):
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return lambda df: make_entries(df, cci_period=cp, sma_period=sp,
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sl_atr_mult=sa, max_bars=mb, use_trend_gate=utg)
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rep = al.study_signals(name, make_fn(), tfs=("1d",))
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v = rep["verdict"]
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min_hold = v.get("best_holdout_sharpe", -999)
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print(f"\n--- Config: {label} ---")
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print(al.fmt(rep))
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print("JSON:", al.as_json(rep))
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if min_hold > best_min_hold:
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best_min_hold = min_hold
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best_rep = rep
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print("\n\n=== BEST CONFIG ===")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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